Retrospective self-reported dietary supplement use by Australian military personnel during deployment to Iraq and Afghanistan: results from the Middle East Area of Operations Health Study
Bibliographic record
Abstract
The use of dietary supplements is popular among military personnel. However, there is a lack of understanding about the changes in use during deployment and the specific factors associated with such changes. This study retrospectively examined changes in the pattern of supplement use among Australian veterans during their deployment to Iraq (n = 8848) and Afghanistan (n = 6507) between 2001 and 2009 and identified work-related circumstances that were associated with these changes. The frequency of use of supplements at present and during deployment was assessed. Multiple logistic regression analysis was used to compare the use of supplements among different groups and among those with different deployment experiences. The study found that overall use of supplements was highest on deployment to Afghanistan (27.8%) compared with deployment to Iraq (22.0%, p < 0.001) or after deployment (current use, 21.2%; p < 0.001). Personnel who were younger or who were at the rank of noncommissioned officer were more likely to use dietary supplements. Men were more likely to use body-building supplements, whereas women more often used weight-loss supplements. Those veterans who did not report using supplements regularly on deployment were far less likely to use them subsequently. Combat exposure, mixed duty cycles, and working long hours during deployment were associated with higher supplement use. The findings confirmed that supplement use in the military reflects the unique demands and stressors of defence service.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".